Week 43: Cost of a Beer at Major League Baseball Stadiums

This week we started with an incredible Makeover Monday Live from #TC18. With over 700 people in attendance, we (1) crashed the wifi and (2) crashed data.world. Well done everyone! Seriously though, this shouldn’t happen and we apologize. We have made our feelings clear to both Tableau and data.world. Thank you for bearing with us and helping each other get the data.

Immediately after the session, we were overwhelmed by the number of people waiting for us to sign their Makeover Monday books. You can order it here. The turnout was amazing for both the session and the book signing and we can’t express enough how much we appreciate each and every one of you. It’s YOU that make the project successful.

With all of the travel back to London and with family in town, I’m going to get right to the lessons. Keep in mind that the lessons this week are based on vizzes that people had an hour to create. I’m merely using them to help us all learn.

LESSON 1: ACCOUNTING FOR MISSING DATA

The data set provided did not include any data for 2017. I could have excluded 2018, but I kept it in to see how people would handle it. Consider this excellent viz from Brian Delehanty:

I love how Brian used the team colors in each area chart. This is very well executed viz. However, he did not account for the gap between 2016 and 2018. Here’s what it looks like if you ensure 2017 is included:

The difference is subtle. Now the spread of the data is correct. Look out for missing dates in your data. To illustrate the lesson a bit more, I’ve change the area chart to a bar chart. First is Brian’s viz and second is the viz including 2017:

Carefully decide how you account for missing data and ensure that you do not mislead your audience. In this case, the difference is so subtle that you may never notice it, but there are other situations where it would be very important to show missing data (e.g., your company didn’t have any sales for a particular day).

LESSON 2: SIZING HEATMAPS

I’m a huge fan of heatmaps. But I like each cell to be nice and tight, usually in a square or a small rectangle. Yanning Wang created this heatmap:

I see a couple problems with this viz:

What does each row represent? I assume a team (because I’m familiar with the data), but for anyone how hasn’t seen the data and only reads the title, they could be confused.

See lesson 1. Notice how the data for from 2016 to 2018.

For me, the boxes are too wide. I would prefer them to be much more narrow.